The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Anonymizing bipartite graph data using safe groupings
Proceedings of the VLDB Endowment
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Class-based graph anonymization for social network data
Proceedings of the VLDB Endowment
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Personalized privacy protection in social networks
Proceedings of the VLDB Endowment
Efficiently anonymizing social networks with reachability preservation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The increasing popularity of social networks in various application domains has raised privacy concerns for the individuals involved. One popular privacy attack is identifying sensitive relationships between individuals. Simply removing all sensitive relationships before releasing the data is insufficient. It is easy for adversaries to reveal sensitive relationships by performing link inferences. Unfortunately, most of previous studies cannot protect privacy against link inference attacks. In this work, we identify two types of link inference attacks, namely, one-step link inference attacks and cascaded link inference attacks. We develop a general framework for preventing link inference attacks, which adopts a novel lineage tracing mechanism to efficiently cut off the inference paths of sensitive relationships. We also propose algorithms for preventing one-step link inference attacks and cascaded link inference attacks meanwhile retaining the data utility. Extensive experiments on real datasets show the satisfactory performance of our methods in terms of privacy protection, efficiency and practical utilities.